Journal of Genetic Genealogy 8(1):38-42, 2016
Editorial
The Shared cM Project: A Demonstration of the Power of
Citizen Science
By: Blaine T. Bettinger, Ph.D., J.D.
http://www.thegeneticgenealogist.com
Abstract
The Shared cM Project (goo.gl/2uouqz) is a collaborative citizen scientist project created to analyze the
ranges of shared centimorgans associated with known genealogical relationships. Between March 2015
and May 2016, members of the genealogical community submitted total shared cM data for almost 10,000
known relationships ranging from parent/child to eighth cousins. The data for each relationship was analyzed to remove extreme outliers, and after determining the minimum reported value, the maximum
reported value, and the average for each relationship, a histogram was generated to reveal the distribution between the minimum and maximum reported values. Although susceptible to data entry errors,
misattributed parentage, endogamy, pedigree collapse, and company thresholds, these known issues are
minimized by the volume of reported values for the majority of relationships. For the first time, genealogists now have observed, non-simulated ranges and distributions of total shared cM data for a wide variety
of relationships based on thousands of data points.
Introduction
One of the most common tasks of a DNA test-taker is to derive possible relationships based on the
total amount of DNA shared between two genetic matches. Although the three major DNA testing
companies (23andMe, AncestryDNA, and Family
Tree DNA) each provide a relationship estimate,
these estimates can vary and may be based on
unclear thresholds. Additionally, relationship estimates may not be available when analyzing shared
DNA using a third-party tool.
and thus does not provide an average, it is a very
good source for relationship prediction.
However, the ISOGG table does not account for
the ranges seen in total shared cM for genealogical
relationships. For example, although the expected
amount of DNA shared by second cousins is 212.50
cM based solely on 50% inheritance at each generation, the actual average and range for tested second
cousins is not provided in the chart. If tested second
cousins share 175 cM, is that unusual or is it common? Does that result support a second cousin relationship, or does it suggest another relationship?
One of the resources used to predict relationships
based on total shared DNA is the Autosomal DNA
Statistics page of the International Society of Genetic Genealogy (ISOGG) Wiki (http://www.isogg.
org/wiki/Autosomal_DNA_statistics). The page has
a variety of sources for relationship predictions, including the table entitled “Average autosomal DNA
shared by pairs of relatives, in percentages and centiMorgans,” which provides the amount of DNA expected to be shared by individuals having a known
genealogical relationship. Although the table assumes exactly 50% inheritance at each generation
There are other sources of data for total shared
DNA for genealogical relationships, but these sources are either based in whole or in part on simulated
data, or they are created using unknown methodologies and must therefore be used with caution.
For example, the “AncestryDNA Matching White
Paper” by Ball et al. (31 March 2016; http://dna.
ancestry.com/resource/whitePaper/AncestryDNA-Matching-White-Paper.pdf) includes Figure 5.2
with the distribution of total shared DNA for a va38
Journal of Genetic Genealogy 8(1):38-42, 2016
● Endogamy and Pedigree Collapse – Some
relationships will be affected by endogamy and/or pedigree collapse, which will
increase the amount of DNA shared by
test-takers having a certain genealogical
relationship. Although the collection form
requests information about known endogamy and pedigree collapse, many contributors will not be aware of the endogamy and
pedigree collapse in their tree.
riety of simulated pedigree relationships. Although
informative, this data is based on simulated data
rather than empirical data. Similarly, the “Average
percent DNA shared between relatives” table published by 23andMe contains data based entirely on
simulations (https://customercare.23andme.com/
hc/en-us/articles/202907170-Average-percentDNA-shared-between-different-types-of-cousins).
Accordingly, there was a need for empirical data for
total shared DNA for genealogical relationships. To
fill this need, the Shared cM Project was launched
on March 4, 2015. A first analysis of the results was
published on May 25, 2016. Discussed herein is an
update to the Shared cM Project. This update includes thousands of additional data points, as well
as total shared cM data for relationships tested by
AncestryDNA. Since AncestryDNA first provided total shared cM data in November of 2015, this update is the first to include this new data.
● Company Thresholds – Each of the DNA
testing companies applies a different
matching threshold to maximize the identification of genetic cousins while minimizing false positives. These thresholds may
impact the total amount of DNA shared by
two test-takers, especially at more distant
relationships.
Despite these issues, the volume of hundreds of
matches (and, hopefully, thousands of matches
in the future) for most relationships in the Shared
cM Project are predicted to minimize the impact of
these issues on the averages and distributions. Accordingly, the Shared cM Project remains the largest collection of empirical data for total shared DNA
for genealogical relationships, and is an example of
the power of citizen science.
The data collected by the Shared cM Project is susceptible to several known issues:
● Data Entry Errors - Some of the information entered by contributors will include
errors resulting from transcribing the data
from the testing company or third-party
tool and entering the data into the field.
For example, for some of the data entries,
the longest segment was greater than the
total shared cM. Although this was most
likely a simple inversion, these data entry
errors were completely removed whenever
they could be identified. Not all errors, of
course, could be reliably identified.
● Incorrect Relationships – Some relationships were most likely entered incorrectly,
which might be due to misunderstandings
of complex genealogical relationships. Other relationship errors are most likely due to
misattributed parentage events resulting
in the believed relationship being incorrect. For example, with the unedited Aunt/
Uncle/Niece/Nephew data, there was a
significant cluster around approximately
850 cM, which is indicative of a half-Aunt/
Uncle/Niece/Nephew relationship. In other
words, there are many unknown half relationships in the data.
Methods and Data
Data Collection
Data was collected from participants using Google Forms, which collected the submissions into a
spreadsheet. The Google Form (available at goo.
gl/qL5BDr) contained data entry fields for required
information (“Known Relationship,” “Total Shared
cM,” “Number of Shared Segments,” “Endogamy
or Known Cousin Marriage” (YES/NO) and “Source”
(AncestryDNA, Family Tree DNA, 23andMe, GEDmatch, or Other)), and optional data entry fields
(“Longest Block,” “Notes,” and “Email Address”).
A total of 9,891 submissions were made to the
Shared cM Project as of May 7, 2016 (beginning
March 4, 2015). For analysis, the submissions were
downloaded as an Excel spreadsheet.
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Journal of Genetic Genealogy 8(1):38-42, 2016
Initial Data Curation
nificant impact on the data, it arguably results in a
dataset with greater reliability.
Because “Known Relationship” was a text entry
field, submissions varied considerably regarding the
naming of various relationships. In this initial data
curation stage, all decipherable relationships were
converted to a uniform format (where “C” equals
cousin and “R” equals removed). Submissions with
indecipherable relationships were eliminated. Submissions with obvious data entry errors were also
eliminated, such as those where the longest segment was longer than the total shared cM, or where
there was text in the cM field instead of a number.
Table 1. Number of Submissions for Each Relationship
Following Outlier Removal
Relationship
This initial data curation eliminated a total of 171
data submissions (1.7%), bringing the total to 9,720
data points used for statistical analysis.
Data Analysis
A total of 34 relationships ranging from Parent/Child
to 8C were analyzed individually. The total number
of submissions for each relationship varied, with a
low of six for great-great-aunt/uncle and a high of
889 for aunt/uncle/niece/nephew. A total of 17 of
the 34 relationships (52.9%) had 100 or more submissions, and 9 of 34 relationships (26.5%) had 500
or more submissions. See Table 1, below.
A box plot was created for each relationship, and
extreme outliers were identified (Q1 - 3*IQR or Q3
+ 3*IQR) and removed from the data. Although this
approach for removing outliers is widely accepted,
outliers should only be removed if there is sufficient justification. A concern with a previous version of data published from the Shared cM Project,
in which outliers remained, was that there were
extreme minimums and maximums which did not
correlate to values actually seen by genetic genealogists and were highly unlikely based on current understanding of genetics. For example, the minimum
for Aunt/Uncle/Niece/Nephew was 121 cM when
outliers were included. Since the expected amount
for this relationship is 1750 cM, the value of 121
cM is most likely due to either an incorrect relationship or a data entry error. Genealogists relying on a
range of Aunt/Uncle/Niece/Nephew as low as 121
cM could make incorrect conclusions. Accordingly,
there was sufficient justification to remove outliers
from the data. Although removing outliers has a sig-
Number of Submissions
Aunt/Uncle/Niece/Nephew
889
2C1R
1C
884
869
2C
1C1R
867
839
3C
794
Siblings
Parent/Child
3C1R
Grandparent/Grandchild
789
758
547
281
4C
1C2R
Half Siblings
2C2R
4C1R
221
193
187
172
164
Great Aunt/Uncle
3C2R
5C
5C1R
Half Aunt/Uncle
158
114
99
82
80
Half 2C
6C1R
7C1R
Half 2C1R
6C
4C2R
Half 1C1R
Great Grandparent/Grandchild
51
46
42
40
38
34
32
29
5C2R
8C
25
25
Half 1C
6C2R
23
20
7C
19
Great Great Aunt/Uncle
Total
6
9,417
Following outlier removal, the dataset contained
9,417 submissions (96.9% of the total 9,720 submissions). The minimum, average, and maximum
values of the remaining data points were identified
for each relationship using standard methodology.
See, Table 2.
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Journal of Genetic Genealogy 8(1):38-42, 2016
Table 2. Minimum, Average, and Maximum Values for
Each Relationship
Relationship
Parent/Child
Siblings
Half Siblings
Grandparent/Grandchild
Great Grandparent/
Grandchild
Aunt/Uncle/Niece/
Nephew
Half Aunt/Uncle
Great Aunt/Uncle
Great Great Aunt/Uncle
1C
Half 1C
1C1R
Half 1C1R
1C2R
2C
Half 2C
2C1R
Half 2C1R
2C2R
3C
3C1R
3C2R
4C
4C1R
4C2R
5C
5C1R
5C2R
6C
6C1R
6C2R
7C
7C1R
8C
#
758
789
187
281
29
Min
3266
2150
1320
1272
547
Average
3471
2600
1753
1765
850
Max
3720
3070
2134
2365
1110
889
1301
1744
2193
80
158
6
869
23
839
32
193
867
51
884
40
172
794
547
114
221
164
34
99
82
25
38
46
20
19
42
25
540
521
214
533
236
115
78
27
43
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
863
857
434
880
554
433
187
235
238
123
129
73
81
79
56
36
31
20
14
17
14
16
9
9
11
7
7
9
1172
1138
580
1379
704
753
253
413
504
245
325
196
201
198
156
82
90
57
27
42
41
41
21
19
29
10
14
16
Figure 3. Histogram showing the distribution of shared
DNA (in centimorgans) between pairs of people who are
aunt/uncle/niece/nephew to one another.
Comparison to Other Data
A comparison of the average values for the data
to the ISOGG Expected Shared DNA table (http://
www.isogg.org/wiki/Autosomal_DNA_statistics)
reveals that the averages are very similar to those
expected.1 However, as discussed above, the expected values do not provide any insight into the
ranges of values observed by test-takers.
Table 3. Comparison of the average values in this study
to the ISOGG Expected Shared DNA table (http://www.
isogg.org/wiki/ Autosomal_DNA_statistics).
Relationship
Parent/Child
Siblings
Half Siblings
Grandparent/Grandchild
Aunt/Uncle/Niece/Nephew
Half Aunt/Uncle
1C
Half 1C
1C1R
2C
Half 2C
2C1R
3C
For relationships where the minimum value was 0
cM shared, the averages were calculated only for
cM amounts greater than 0 cM. Accordingly, these
averages represent the average only for cousins actually sharing a detectable amount of DNA.
A histogram was created relationships with 100 or
more submissions (with the exception of half aunt/
uncle, which had 80 submissions). The histograms
were created in Excel using the data for each relationship with outliers removed. An example of the
histogram for Aunt/Uncle/Niece/Nephew is show
below:
1
41
Shared cM
Project
(Average)
3471
2600
1753
1765
1744
863
880
554
433
238
123
129
79
ISOGG
Table
(Expected)
3400
2550
1700
1700
1700
850
850
425
425
213
106
106
53
A comparison of the average values for the data to the ISOGG
Expected Shared DNA table (http://www.isogg.org/wiki/ Autosomal_DNA_statistics) reveals that the averages are very similar to those expected (Table 3).
Journal of Genetic Genealogy 8(1):38-42, 2016
Future Directions
Larger Datasets – As genealogists continue to test
family members, the number of submissions to the
Shared cM Project continues to grow. In the future,
it will be advantageous to repeat this analysis using
a greater number of submissions, especially for relationships that are underrepresented in the present version.
There are many possible avenues for future research and analysis using the Shared cM Project
dataset, which continues to grow. Among these
possibilities are the following:
Source Breakdown – One of the variables reported
for each submission was the source of the information (23andMe, AncestryDNA, Family Tree DNA,
or GEDmatch). Determining minimum, maximum,
and average values for each testing company and
third-party tool individually may reveal important
differences.
Conclusion
The Shared cM Project offers empirical data on DNA
sharing that complement existing theoretical and
simulated resources for autosomal genealogy tests.
It does so by harnessing the power of citizen scientists to amass sufficient data for analysis. The Project can serve as a model for similar group projects
to address questions of importance to the genetic
genealogy community.
Endogamy Breakdown – Another variable reported for each submission was whether there was any
known endogamy or pedigree collapse in the family
tree that could affect the amount of DNA shared
by the two test-takers. The current analysis used
submissions regardless of their endogamy status.
Known endogamy or pedigree collapse is hypothesized to increase the average amount of DNA
shared by test-takers compared to those without
known endogamy or pedigree collapse.
Conflicts of Interest
Blaine Bettinger is an author, educator, and blogger
on topics related to genetic genealogy. As a lawyer,
he also represents GEDmatch, Inc. before the U.S.
Patent and Trademark Office. He declares no conflicts of interest.
Group by Clusters – Grouping the data by relationships that share comparable amounts of DNA (rather than by individual relationships) before performing the data analysis may be beneficial. Each cluster
will have significantly more submissions than individual relationships. Potential clusters are shown in
Table 4.
Table 4. Potential clusters of relationships sharing similar
amounts of DNA.
Cluster
Included Relationships
1
Parent/Child
2
Siblings
Half Siblings, Grandparent/Grandchild,
3
Aunt/Uncle/Niece/Nephew
4
5
6
7
8
9
10
Great Grandparent/Grandchild, Half Aunt/Uncle/
Niece/Nephew, Great Aunt/Uncle/Niece/Nephew,
1C
Half 1C, 1C1R
Half 1C1R, 1C2R, 2C
Half 2C, 2C1R
Half 2C1R, 2C2R, 3C
3C1R
3C2R, 4C
42